#= This line adds functions to take
an AR(2) model for US inflation
=#
using DataFrames
function lag0(x,p)
R::Int32=size(x,1)
C::Int32=size(x,2)
# Take the first R-p rows of matrix x
x1=x[1:(R-p),:]
return out=[zeros(p,C); x1]
end
#load inflation datatable
#df=readtable("inflation.csv")
df=DataFrame()
df[:A]=1:8
df[:B]=10:17
##################################
# Way of converting DataArray to Array #
##################################
Y=[df[i,2] for i in [1:size(df,1)]]
## >>>> type of (Y) is now "Any"
T=size(Y,1)
X=[ones(T,1) lag0(Y,1) lag0(Y,2)]
inv(X'*X)
==========================================================
Hi All,
I am posting again the issue. I found when I create an Array as shown
above, the type of Array
changed to "Any" which is not acceptable for the base Inverse function(an
error occurred).
I am wondering why Julia does not assume the type of original data, here in
the example, Int64.
Many thanks,
On Thursday, June 18, 2015 at 7:23:16 PM UTC+2, SG wrote:
>
> Thank you, I will cut it down and post it again soon.
>
> On Thursday, June 18, 2015 at 7:01:16 PM UTC+2, Stefan Karpinski wrote:
>>
>> I think these sample programs may be too big for people to review for
>> you. If you can pare the problem down to an example that can be posted in
>> an email, you've more likely to get help.
>>
>> On Thu, Jun 18, 2015 at 11:11 AM, SG <[email protected]> wrote:
>>
>>>
>>> Hi All,
>>>
>>> I am a novice to Julia.
>>>
>>> While I am running the attached julia program, I found "inverse"
>>> function throws an error?
>>> However it worked well in Matlab. Can you help me? Many thanks.
>>>
>>>
>>>
>>>
>>
